Question

我的实验的大日志文件（.txt）（包含多达10万个条目）具有以下结构：

ROUTINE    TEMPERATURE    VOLTAGE    WAVELENGTH
_______________________________________________
CHANGE T   75             0          560
CHANGE T   80             0          560
CHANGE T   85             0          560
CHANGE T   90             0          560
OSL        75             20         570
OSL        75             20         580
OSL        75             20         590
OSL        75             20         600
CHANGE T   75             0          560
CHANGE T   80             0          560
CHANGE T   85             0          560
CHANGE T   90             0          560

我使用 pandas 中的 read_table 将日志文件加载到python中。我想根据第一列的值将结果数据帧分成较小的数据帧。所以结果看起来像这样：

**DATAFRAME 1:**    
CHANGE T   75             0          560
CHANGE T   80             0          560
CHANGE T   85             0          560
CHANGE T   90             0          560

**DATAFRAME 2:** 
OSL        75             20         570
OSL        75             20         580
OSL        75             20         590
OSL        75             20         600

**DATAFRAME 3:** 
CHANGE T   75             0          560
CHANGE T   80             0          560
CHANGE T   85             0          560
CHANGE T   90             0          560

首先，我尝试使用第一列值更改的索引来拆分它们：

indexSplit = [] # list containing the boundry indices

prevRoutine = log['ROUTINE'][0] # log is the complete dataframe
i = 1
while i < len(log):
        if prevRoutine != log['ROUTINE'][i]:
            indexSplit.append(i)
        prevRoutine = log['ROUTINE'][i]

然而，考虑到日志文件的大小，这样做需要花费大量时间（显然）。我想知道是否有一种优雅的方式与熊猫一起做到这一点？我一直遇到的问题是第一列的值在多个系列中使用。我总是将 dataframe 1 和 dataframe 3 作为一个结果。

Answer 1

您可以使用list comprehension groupby来创建循环groups对象和s。比较ne（与!=相同但速度更快）shift ed列和cumsum得到输出：

s = df['ROUTINE'].ne(df['ROUTINE'].shift()).cumsum()
print (s)
0     1
1     1
2     1
3     1
4     2
5     2
6     2
7     2
8     3
9     3
10    3
11    3
Name: ROUTINE, dtype: int32

dfs = [g for i,g in df.groupby(df['ROUTINE'].ne(df['ROUTINE'].shift()).cumsum())]
print (dfs)
[    ROUTINE  TEMPERATURE  VOLTAGE  WAVELENGTH
0  CHANGE T           75        0         560
1  CHANGE T           80        0         560
2  CHANGE T           85        0         560
3  CHANGE T           90        0         560,   ROUTINE  TEMPERATURE  VOLTAGE  WAVELENGTH
4     OSL           75       20         570
5     OSL           75       20         580
6     OSL           75       20         590
7     OSL           75       20         600,      ROUTINE  TEMPERATURE  VOLTAGE  WAVELENGTH
8   CHANGE T           75        0         560
9   CHANGE T           80        0         560
10  CHANGE T           85        0         560
11  CHANGE T           90        0         560]

print (dfs[0])
    ROUTINE  TEMPERATURE  VOLTAGE  WAVELENGTH
0  CHANGE T           75        0         560
1  CHANGE T           80        0         560
2  CHANGE T           85        0         560
3  CHANGE T           90        0         560

print (dfs[1])
  ROUTINE  TEMPERATURE  VOLTAGE  WAVELENGTH
4     OSL           75       20         570
5     OSL           75       20         580
6     OSL           75       20         590
7     OSL           75       20         600

print (dfs[2])
     ROUTINE  TEMPERATURE  VOLTAGE  WAVELENGTH
8   CHANGE T           75        0         560
9   CHANGE T           80        0         560
10  CHANGE T           85        0         560
11  CHANGE T           90        0         560

解决方案很复杂，因为如果第一列使用groupby只能获得2组：

dfs = [g for i,g in df.groupby('ROUTINE')]
print (dfs)
[     ROUTINE  TEMPERATURE  VOLTAGE  WAVELENGTH
0   CHANGE T           75        0         560
1   CHANGE T           80        0         560
2   CHANGE T           85        0         560
3   CHANGE T           90        0         560
8   CHANGE T           75        0         560
9   CHANGE T           80        0         560
10  CHANGE T           85        0         560
11  CHANGE T           90        0         560,   ROUTINE  TEMPERATURE  VOLTAGE  WAVELENGTH
4     OSL           75       20         570
5     OSL           75       20         580
6     OSL           75       20         590
7     OSL           75       20         600]

Python：如何根据第一列中的值将pandas DataFrame拆分为子集？

1 个答案: